Identify Relative importance of covariates in Bayesian lasso quantile regression via new algorithm in statistical program R

In this paper, we propose a new algorithm to determine the relative importance of covariates by Bayesian Lasso quantile regression for variable selection assigning new formula of Laplace distributions for the regression parameters. Simple and efficient Markov chain Monte Carlo (M.C.M.C) algorithm wa...

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Bibliographic Details
Published inRevista română de statistică Vol. 65; no. 4; pp. 99 - 110
Main Authors Fadel Hamid Hadi Alhusseini, Taha al Shaybawee, Fedaa Abd Almajid Sabbar Alaraje
Format Journal Article
LanguageEnglish
Published Romanian National Institute of Statistics 01.11.2017
Subjects
Bayesian lasso quantile regression
MCMC algorithm
posterior distributions
Prior distributions
Relative importance
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ISSN1018-046X
1844-7694

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Summary:In this paper, we propose a new algorithm to determine the relative importance of covariates by Bayesian Lasso quantile regression for variable selection assigning new formula of Laplace distributions for the regression parameters. Simple and efficient Markov chain Monte Carlo (M.C.M.C) algorithm was introduced for Bayesian sampler. Simulation approaches and two real data set are used to assess the performance of the proposed method. Both simulated and real data sets show that the performs of the proposed method is quite good for Identify Relative importance of covariates.
ISSN:1018-046X
1844-7694